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VIČAR, T. HEJČ, J. NOVOTNÁ, P. RONZHINA, M. JANOUŠEK, O.
Originální název
ECG Abnormalities Recognition Using Convolutional Network With Global Skip Connections and Custom Loss Function
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
The latest trends in clinical care and telemedicine suggest a demand for a reliable automated electrocardiogram (ECG) signal classification methods. In this paper, we present customized deep learning model for ECG classification as a part of the Physionet/CinC Challenge 2020. The method is based on modified ResNet type convolutional neural network and is capable to automatically recognize 24 cardiac abnormalities using 12-lead ECG. We have adopted several preprocessing and learning techniques including custom tailored loss function, dedicated classification layer and Bayesian threshold optimization which have major positive impact on the model performance. At the official phase of the Challenge, our team BUT-Team - reached a challenge validation score of 0.696, and the full test score of 0.202, placing us 21 out of 40 in the official ranking. This implies that our method performed well on data from the same source (reached first place with validation score), however, it has very poor generalization to data from different sources.
Klíčová slova
ECG, arrhythmia, signal, classification, challenge, CinC 2020, Computing in Cardiology
Autoři
VIČAR, T.; HEJČ, J.; NOVOTNÁ, P.; RONZHINA, M.; JANOUŠEK, O.
Vydáno
30. 9. 2020
Nakladatel
IEEE
Místo
NEW YORK
ISBN
978-1-7281-7382-5
Kniha
Computing in Cardiology 2020
Edice
47
Číslo edice
1
ISSN
2325-8861
Periodikum
Compuing in Cardiology 2013
Stát
Španělské království
Strany od
Strany do
4
Strany počet
URL
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9344393
BibTex
@inproceedings{BUT165485, author="Tomáš {Vičar} and Jakub {Hejč} and Petra {Novotná} and Marina {Filipenská} and Oto {Janoušek}", title="ECG Abnormalities Recognition Using Convolutional Network With Global Skip Connections and Custom Loss Function", booktitle="Computing in Cardiology 2020", year="2020", series="47", journal="Compuing in Cardiology 2013", number="1", pages="1--4", publisher="IEEE", address="NEW YORK", doi="10.22489/CinC.2020.189", isbn="978-1-7281-7382-5", issn="2325-8861", url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9344393" }